Image processing method and device for auto white balance

ABSTRACT

An auto white balance method is disclosed. The auto white balance method includes receiving image data to divide the image data into a plurality of partition cells, calculating an estimation value of a skin tone included in the image data, and selecting outliers for detection of a white point of the image data, based on at least some of the plurality of partition cells and the estimation value.

CROSS-REFERENCE TO RELATED APPLICATION

This is a Continuation of U.S. application Ser. No. 16/057,856, filedAug. 8, 2018, and a claim of priority is made to Korean PatentApplication No. 10-2017-0117228 filed on Sep. 13, 2017, in the KoreanIntellectual Property Office, the subject matter of which is herebyincorporated by reference.

BACKGROUND

The inventive concept relates to image processing methods and devices.More particularly, the inventive concept relates to image processingmethods and devices performing auto white balance on image data.

Auto white balance (AWB) is an image processing function that may beused to control the imaging and/or reproduction (e.g., display orprinting) of an image, such that “white” (or lighter or brighter)portions or objects of the image may be appropriately visualized asbeing white, despite variations in image staging, color and/or imageillumination (e.g., sunlight, fluorescent or incandescent lights, etc.).Effective AWB is often necessary to correct imaging and/or imagereproductions where reproduced white portions or objects otherwiseexhibit poor or undesirable coloring (e.g., bluish or reddish tinting ofwhite portions).

This necessity arises from the recognize phenomenon that while the humaneye is able to visually adapt to variations in staging, colors, and/orillumination, electronic imaging apparatuses (e.g., cameras) do notpossess an innate ability to visually adapt in order to correct ofcompensate for varying image conditions. AWB is a technology commonlyused to correct or compensate for various phenomena characterized by anundesired color shift where a color is changed in cameras depending onilluminants.

SUMMARY

The inventive concept provides an image processing method and device forauto white balance.

According to an aspect of the inventive concept, there is provided anauto white balance method performed by an image signal processor, theauto white balance method including; dividing image data into aplurality of partition cells, calculating a skin tone estimation valuefor the image data, and selecting outliers for detection of a whitepoint of the image data based on at least one of the plurality ofpartition cells and the skin tone estimation value.

According to an aspect of the inventive concept, there is provided anauto white balance method including; dividing image data including aface image into a plurality of partition cells, calculating a skin toneestimation value based on selected partition cells included in the faceimage, and selecting a gray candidate cluster for performing auto whitebalance on the image data based on at least one of the selectedpartition cells and the estimation value.

According to an aspect of the inventive concept, there is provided anauto white balance method performed by an image signal processor, theauto white balance method including; dividing the image data including aface image into a plurality of partition cells, selecting partitioncells associated with at least a portion of the face image from theplurality of partition cells, calculating a skin tone estimation valuebased on at least one of the selected partition cells, further selectingfrom among the selected partition cells those selected partition cellsincluded in a first gray candidate cluster defined in a color spaceassociated with the image data, and selecting a second gray candidatecluster for performing the auto white balance on the image data, basedon the further selected partition cells included in the first graycandidate cluster and the skin tone estimation value.

According to an aspect of the inventive concept, there is provided anauto white balance method performed by an image signal processor, theauto white balance method including; dividing image data including aface image into a plurality of partition cells, detecting a face regionassociated with the face image from the image data, selecting partitioncells associated with the face image from among the plurality ofpartition cells to define a set of selected partition cells, furtherdefining the set of selected partition cells to define a set of furtherselected partition cells, and calculating a skin tone estimation valuefor the image data from the further selected partition cells.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the inventive concept will be more clearly understoodfrom the following detailed description taken in conjunction with theaccompanying drawings in which:

FIG. 1 is a block diagram of an image processing device according to anexemplary embodiment;

FIG. 2 is a flowchart illustrating an auto white balance methodaccording to an exemplary embodiment;

FIG. 3 is a block diagram for describing an example of a partition cellillustrated in FIGS. 1 and 2;

FIG. 4 is a flowchart illustrating an example of operation S20 of FIG. 2according to an exemplary embodiment;

FIGS. 5A and 5B are diagrams for describing an operation of selectingsome partition cells included in a face region, according to anexemplary embodiment;

FIGS. 6A and 6B are diagrams for describing an operation of selectingsome partition cells included in a face region, according to anexemplary embodiment;

FIGS. 7A and 7B are diagrams for describing an operation of selectingsome partition cells included in a face region based on luminance,according to an exemplary embodiment;

FIGS. 8A to 8C are diagrams for describing an example of operation S130of FIG. 4 according to an exemplary embodiment;

FIG. 9 is a flowchart illustrating an example of operation S30 of FIG. 2according to an exemplary embodiment;

FIG. 10 is a graph showing a first gray candidate cluster defined in acolor space according to an exemplary embodiment;

FIG. 11 is diagram for describing an operation of selecting a secondgray candidate cluster, according to an exemplary embodiment;

FIG. 12 is diagram for describing an operation of selecting a secondgray candidate cluster, according to another exemplary embodiment;

FIG. 13 is diagram for describing an operation of selecting a secondgray candidate cluster, according to another exemplary embodiment;

FIG. 14 is a diagram for describing a second gray candidate clusterselected according to an exemplary embodiment;

FIG. 15 is a diagram illustrating an example where partition cellsincluded in a second gray candidate cluster selected according to anexemplary embodiment are displayed on an actual image;

FIG. 16 is a flowchart illustrating an operation of an image signalprocessor according to an exemplary embodiment;

FIG. 17 is a flowchart for describing an operation based on a weightvalue added according to an exemplary embodiment; and

FIG. 18 is a diagram illustrating an example of a mobile device equippedwith an image signal processor according to an exemplary embodiment.

DETAILED DESCRIPTION

Hereinafter, exemplary embodiments will be described with reference tothe accompanying drawings.

FIG. 1 is a general block diagram of an image processing device 1according to an exemplary embodiment. The image processing device 1 maycorrespond to various systems incorporating an image signal processor,such as portable camcorders, smartphones and the like.

Referring to FIG. 1, the image processing device 1 comprises an imagesensor 10 and an image signal processor 20. The image sensor 10 may beused to receive (or capture, or sense) an image IMG using (e.g.,) alens, focusing circuitry, filters, etc. (not shown). Upon sensing theimage IMG, the image sensor 10 may output corresponding image dataD_IMG. The image data D_IMG may include various color data, such as thecolor and/or contrast information commonly associated RGB approaches orYUV approaches to image processing.

The image sensor 10 may include, for example, a charge-coupled device(CCD) and/or a complementary metal-oxide semiconductor (COMS) imagesensor (CIS). For example, if the image sensor 10 includes a CIS, theimage sensor 10 may include a pixel array, and a plurality of pixelsincluded in the pixel array may each include a photo sensing element,where each photo sensing element generates an electrical signal inresponse (e.g.,) to the intensity of captured electromagnetic energy(e.g., optical light).

The image signal processor 20 may be used to receive the image dataD_IMG from the image sensor 10, perform an RGB interpolation of theimage data D_IMG in order to generate color component data RGB data(e.g., red (R), green (G), and/or blue (B) component data).Additionally, the image signal processor 20 may perform a YUV conversionin response to the RGB data and/or the image data D_IMG in order togenerate YUV data (e.g., luminance (Y) and/or chrominance (U, V)signals. The image signal processor 20 may also perform various digitalsignal processing, such as contour emphasis for enhancing image quality.The image signal processor 20 may also be used to selectively divide theimage data D_IMG into a plurality of partition cells and thereafter toperform various digital signal processing on one or more of theplurality of partition cells.

In an exemplary embodiment, each of the plurality of partition cells maybe a single pixel or a patch including a plurality of pixels. Data ofvarious types, formats and uses obtained by digital image processingperformed by the image signal processor 20 may be stored in one or morememories (not shown, but e.g., a synchronous dynamic random accessmemory (SDRAM) or the like).

In addition to the foregoing, the image signal processor 20 may be usedto perform a so-called auto white balance function (hereafter, “AWB”) onthe image data D_IMG. AWB is a function frequently associated with thedigital image processing of image data D_IMG. During AWB a white pointis identified among the image data D_IMG and a color sense adjustment ismade to the image data D_IMG in relation to the white point. Forexample, the image data D_IMG may be adjusted in response to the gain ofa chrominance component (R or B) determined in relation to the whitepoint. Following AWB, the image signal processor 20 may outputAWB-processed image data D_AWB.

In the illustrated example of FIG. 1, it is assumed that AWB isperformed by an AWB module 22 of the image signal processor 20. In thiscontext, the AWB module 22 be variously configured in hardware (e.g.,through logical synthesis) and/or software (e.g., as firmware executedby the image signal processor 20).

Extending the description of the foregoing example, the image signalprocessor 20 may be used to detect a white point from the image dataD_IMG. In certain embodiments, the detected white point may be dataassociated with a greatest (or largest) color change or signal intensitydue to (e.g.,) an illumination source (or illuminant). In this regard,the image signal processor 20 may be used to determine a colortemperature at a given RGB ratio for the detected white point.Subsequently, by adjusting gains for R and B components of the imagedata D_IMG according to the determined color temperature, a color sensefor the image may be appropriately shifted. In this manner, as oneexample, color correction or color balance adjustment may be performedfor the image data D_IMG.

Here, when color temperature for the image data D_IMG is changed by anilluminant, an adjustment may be made, whereby the detected white pointis visually reproduced to remain white in appearance, rather than red orblue tinted, for example. That is, an adjustment by increasing a gainfor R and decreasing a gain for B may be used to reduce an undesiredblue appearance of the white point, or an adjustment by increasing thegain of B and decreasing the gain of R may be used to reduce anundesired red appearance of the white point.

The image signal processor 20 of FIG. 1 may also be used to select awhite point corresponding to various criterion associated with the AWBbeing performed. However, when a white point is selected (or detected)by the image signal processor 20 from image data D_IMG including imagedata corresponding to a skin tone area of the image, difficulties inperforming a conventional AWB may arise. That is, when an estimationvalue associated with a white point detected from a skin tone region ofthe image data D_IMG is obtained, for example, a subsequently performed,conventional AWB may be performed with unacceptable results. Incontrast, exemplary embodiments may be used to more accurately perform aAWB in relation to image data D_IMG including skin tone image data, suchas a face. Thus, the image signal processor 20 may be used to calculatean estimation value associated with skin tone related image data, usingfor example a face portion of the a captured image (hereafter, “skintone estimation value”), and thereafter select an outlier of a whitepoint based, at least in part, on the skin tone estimation value. Usingthis approach, the image signal processor 20 of FIG. 1 may moreaccurately select a white point serving as a data predicate for an AWBperformed in relation to the image data D_IMG.

FIG. 2 is a flowchart generally summarizing an AWB method according toan exemplary embodiment. The AWB method of FIG. 2 may be performed bythe image signal processor 20 of FIG. 1.

Referring to FIG. 2, the image signal processor 20 may receive the imagedata D_IMG and may divide the image data D_IMG into a plurality ofpartition cells (S10). Each of the plurality of partition cells may be,for example, a pixel or a patch including a plurality of pixels.Alternatively, each partition cell may be a block including a pluralityof patches. Each partition cell may include a plurality of vertexes.

After partitioning of the image data D_IMG into the plurality ofpartition cells, the image signal processor 20 may be used to calculatea skin tone estimation value associated with a skin tone included in thereceived image data D_IMG (S20). In certain exemplary embodimentswherein a face image is included in the image data D_IMG, the imagesignal processor 20 may calculate the skin tone estimation value basedon image data associated with the face image.

For example, the image signal processor 20 may select one or morepartition cells from among the plurality of partition cells that includeat least part of an identified face image (hereafter, “selectedpartition cells”), and thereafter calculate a skin tone estimation valuebased (e.g.,) on one or more selected color component(s) for each one ofthe selected partition cells. In an exemplary embodiment, the skin toneestimation value may be calculated as a mean value derived from colorcomponent(s) of the selected partition cells.

After the skin tone estimation value is calculated, the image signalprocessor 20 may be used to select an outlier for detection of a whitepoint of the image data D_IMG based on at least one of the plurality ofpartition cells included in the image data D_IMG and the skin toneestimation value (S30). In an exemplary embodiment, the outlier mayassociated with one or more partition cell(s) otherwise excluded fromthe white point detecting process. That is, the image signal processor20 may use one or more partition cells that were not used to calculatethe skin tone estimation value (hereafter, “non-selected partitioncells”) to determine the outlier.

For example, the white point may be determined in relation to a mean ofcolor components of non-selected partition cells included in a graycandidate cluster among the plurality of partition cells included in theimage data. In an exemplary embodiment, the image signal processor 20may identify certain non-selected partition cells included in a firstgray candidate cluster defined in a color space from among the pluralityof partition cells included in the image data D_IMG, and may thereafterdetermine the (a first) outlier from the first gray candidate clusterbased on the skin tone estimation value. The image signal processor 20may also identify as a second gray candidate cluster of non-selectedpartition cells outside the first gray candidate cluster and determineanother (a second) white point based on the non-selected partition cellsincluded in the second gray candidate cluster.

FIG. 3 is a conceptual diagram further illustrating one approach to thedefinition of the plurality of partition cells in the context of theexemplary embodiments previously described in relation to FIGS. 1 and 2.

Referring to FIG. 3, an image IMG may be divided into a plurality ofpatches PC, where each patch PC includes a plurality of pixels PX. Theimage IMG includes (or may be defined in relation to) informationassociated with various color and/or luminance values used to displaythe image on a display device for some duration of time (e.g., onescreen, or one frame). That is, each of the pixels PX included in theimage IMG may produce one or more electrical signals quantified by dataindicating one or more color and/or luminance component(s). For example,pixels PX generating the image data corresponding to the captured imageIMG may be characterized by and operate according to one or moreconventionally understood video standards such as RGB or YUV, etc.

In an exemplary embodiment, the image signal processor 20 may perform anAWB according to patch units or pixel units. For example, if the AWB isperformed by patch units, the image signal processor 20 may calculate askin tone estimation value based on color information associated withone or more selected patches, determine one or more white points fromnon-selected patches, and determine one or more outliers based on thewhite point(s) and the skin tone estimation value. Hereinafter, forconvenience of description, examples are described assuming that an AWBis performed according to patch units.

FIG. 4 is a flowchart summarizing in one example the step of calculatinga skin tone estimation value (S20) of FIG. 2 according to an exemplaryembodiment. The method described in relation to FIG. 4 may be performed,for example, by the image signal processor 20 of FIG. 1.

Referring to FIGS. 1 and 4, the image signal processor 20 may receiveimage data D_IMG including data associated with at least a portion of aface image contained in a captured image. (S100).

Thereafter, the image signal processor 20 may be used to detect a faceregion associated with the face image from the image data D_IMG (S110).For example, the detection of the face image and corresponding faceregion may be performed using a face detector 24 shown in FIG. 1 asbeing included in the image signal processor 20. The face detector 24may perform face detection on the image data D_IMG for detecting atleast a portion of a face having a feature close to a pre-stored modelpattern such as a contour, a color, or the like of the face. Also, theface detector 24 may generate coordinate information identifying thedetected face region, where the generated coordinate information may beused to perform the AWB. Here, the face detector 24 may be implementedin hardware (e.g., as a logical synthesis) and/or in software (e.g.,firmware executed by a processor).

In order to detect the face region, the face detector 24 may detect oneor more relatively invariable feature(s) (e.g., eyes, nose, and mouth,texture, skin tone, etc.) of the face using a feature-based facedetection method. Thereafter, the face detector 24 may calculate featurepoint coordinates associated with the face. Alternatively, the facedetector 24 may detect the face region using a face template-baseddetection method, or may detect the face region by using a supportvector machine (SVM) method which learns a face and a part other thanthe face by using a plurality of sample images, and then, detects a faceregion from input image data D_IMG.

However, face detection and corresponding point coordinate generation isperformed, the image signal processor 20 may thereafter select one ormore partition cells from among a plurality of partition cells includedin the face region (S120). These face region associated partition cellsmay be termed “selected partition cells” or “first-selected partitioncells” to distinguish them from “non-selected partition cells”. Thus, inan exemplary embodiment, the image signal processor 20 may define asselected partition cells those partition cells having some or allconstituent vertexes included in a face region from among the pluralityof partition cells.

Moreover, the image signal processor 20 may exclude as non-selectedpartition cells those partition cells having luminance values less thanor equal to a threshold luminance value from among the plurality ofpartition cells. In an exemplary embodiment, the threshold luminancevalue may be a value that is derived by statistical analysis of sampleimage data.

After the selected partition cells have been identified, the imagesignal processor 20 may calculate a skin tone estimation value based oncolor components associated with the selected partition cells (S130). Inan exemplary embodiment, the image signal processor 20 may calculate aglobal mean value of the color components associated with the selectedpartition cells and compare the global mean value with the colorcomponent(s) of each of the selected partition cells. In this manner,the image signal processor 20 may exclude certain selected partitioncells having a color component having a difference with respect to theglobal mean value that is greater than a reference deviation.Accordingly, the skin tone estimation value may be calculated using onlyselected partition cells having a color component within a specifiedrange, such that the resulting skin tone estimation value may becalculated as a final mean value of the color components forappropriately designated selected partition cells.

FIG. 5A is a flowchart and FIG. 5B is a conceptual diagram furtherdescribing the selection of certain partition cells as selectedpartition cells associated with a detected face region according to anexemplary embodiment. Here, FIG. 5A further illustrates one example ofmethod step S120 of FIG. 4. FIG. 5B illustrates an exemplary imagedivided in partition cells (patches) that may be selected using themethod of FIGS. 4 and 5A.

Referring to FIGS. 1, 5A and 5B, the image signal processor 20 may beused to extract vertex information associated with each of the pluralityof partition cells included in a face region F (S121). The face region Fmay be a region detected by the image signal processor 20 based on aface image. For example, the face region F may be detected by the facedetector 24 included in the image signal processor 20 and may includemultiple partition cells. That is, the face region F may includemultiple partition cells having at least one vertex included in (orfalling within) the detected face image from among the plurality ofpartition cells forming the image IMG.

Once vertex information associated with selected partition cells fallingwithin the face region has been extracted, the image signal processor 20may further select certain partition cells FC having all vertexesincluded in the face image from among the plurality of selectedpartition cells falling within the face region F (S122). In other words,all the vertexes of the further selected (or “second-selected”)partition cells FC may be included in the face image. As anotherexample, the image signal processor 20 may further select partitioncells having (e.g.,) three or more vertexes falling within the faceimage from among the plurality of selected partition cells.

In an exemplary embodiment, the image signal processor 20 may calculatea skin tone estimation value based on color component(s) of at least oneof the second-selected partition cells FC. In an exemplary embodiment,the skin tone estimation value may be calculated as a mean value ofcolor component(s) of at least one of the second-selected partitioncells FC.

First-selected partition cells, other than the second-selected partitioncells FC, associated with the face region F may be excluded from thecalculation of the skin tone estimation value. Therefore, allnon-selected partition cells from the image IMG (e.g., non-facial headregions, background regions, etc.) that are not associated with the faceimage may also be excluded from the calculation of the skin toneestimation value, thereby improving an accuracy of the estimation value.

FIG. 6A is another flowchart and 6B is another conceptual diagramfurther describing in another approach the selecting of certainpartition cells included in a face region according to an exemplaryembodiment. More particularly, FIG. 6A is a flowchart illustratinganother embodiment that may be used to select partition cells (S120 ofFIG. 4). FIG. 6B illustrates another exemplary image in relation toselected partition cells.

In the context of the example previously described in relation to FIGS.5A and 5B and referring to FIGS. 1, 6A and 6B, the image signalprocessor 20 may be used to extract vertex information associated withpartition cells included in (falling within) a face region F′ (S121′).Here, selected partition cells FC′ are those having all vertexesincluded in the face image F′ (S122′). However, certain of theseotherwise (or possibly) selected partition cells are excluded, namelythose partition cells including an eye or eyebrow image from among theselected partition cells FC′ (S123′). Thus, all partition cells havingall constituent vertexes included in the face image are selected ordesignated as selected partition cells FC′, except those partition cellsincluding the eye or eyebrow image. In this context, informationregarding eye and eyebrow features as well as corresponding imagecoordinate information may be generated by the face detector 24 includedin the image signal processor 20.

FIGS. 7A and 7B are diagrams describing exemplary embodiments whereinselection of certain partition cells included in a face region is basedon luminance. That is, FIG. 7A is a flowchart summarizing anotherapproach to the method step S120 of FIG. 4, and FIG. 7B is a conceptualdiagram illustrating a corresponding array of luminance values for aplurality of partition cells.

Referring to FIGS. 1, 7A and 7B, the image signal processor 20 may beused to extract respective luminance values for partition cells includedin a face region. That is, the image signal processor 20 may extractluminance values for first-selected partition cells FC according to theselection operation described above with reference to FIG. 5 forexample.

Each of the first-selected partition cells FC has a respective luminancevalue that may be obtained (e.g.,) by digitizing luminance informationassociated with a captured image (S124). That is, an image will usuallyinclude partition cells characterized by relatively low luminance valuesassociated with darker portions of the image as well as other partitioncells characterized by relatively high luminance values associated withbrighter portions of the image.

Once respective luminance values have been extracted, the image signalprocessor 20 may exclude certain partition cells having a correspondingluminance value less than or equal to a threshold luminance value fromthe calculation of the skin tone estimation value (S125).

For example, assuming a threshold luminance value of 500, the imagesignal processor 20 may calculate a skin tone estimation value based onfirst-selected partition cells FC, excluding those first-selectedpartition cells D_FC having a corresponding luminance value equal to orless than 500. This is another type of second selection of partitioncells from an initial set of first-selected partition cells providing amore accurate skin tone estimation value. That is, partition cellsincluding darker image portions may be excluded from the calculation ofthe skin tone estimation value, thereby improving accuracy of theestimation value.

FIGS. 8A, 8B and 8C are diagrams collectively describing differentapproached to the method step S130 of FIG. 4 according to exemplaryembodiments. FIG. 8A is a flowchart summarizing the calculation of askin tone estimation value according to an exemplary embodiment, FIG. 8Bis a graph showing distribution of color components for selectedpartition cells, and FIG. 8C is a pseudo code example that may be usedto calculate the skin tone estimation value.

Referring to FIGS. 1 and 8A, the image signal processor 20 may be usedto calculate a global mean value of color components of selectedpartition cells (S132). The selected partition cells may be, forexample, partition cells for which the operation described above withreference to FIGS. 5A to 7B have been performed. The color components ofthe selected partition cells may each include, for example, a redcomponent and a blue component. The image signal processor 20 may beused to calculate a mean value of the red components of the selectedpartition cells as a global mean value of the red components and maycalculate a mean value of the blue components of the selected partitioncells as a global mean value of the blue components.

Subsequently, the image signal processor 20 may be used to color-detect(another form of further selection of partition cells) certain selectedpartition cells, having a color component where a difference withrespect to a global mean value is greater than the reference deviation,from among the selected partition cells (S134). In an exemplaryembodiment, the image signal processor 20 may compare the global meanvalue of the red components with the red component of each of theselected partition cells and may detect partition cells having a redcomponent where a difference with respect to the global mean value ofthe red components is greater than the reference deviation, based on aresult of the comparison. Alternately or additionally, the image signalprocessor 20 may compare the global mean value of the blue componentswith the blue component of each of the selected partition cells and maydetect partition cells having a blue component where a difference withrespect to the global mean value of the blue components is greater thanthe reference deviation, based on a result of the comparison.

In an exemplary embodiment, the reference deviation may be amultiplication of a tuning parameter and a variance of each of the colorcomponents of the selected partition cells in a color space. Forexample, the image signal processor 20 may be used to calculate avariance of the red component and a variance of the blue component ofeach of the selected partition cells. The tuning parameter may becalculated based on statistical processing of pieces of sample imagedata, for example.

The reference deviation may be calculated as at least one of amultiplication of a tuning parameter and a variance of a red componentand a multiplication of a tuning parameter and a variance of a bluecomponent. For example, in a case of detecting partition cells having ared component where a difference with respect to a global mean value ofred components is greater than the reference deviation, the image signalprocessor 20 may calculate the reference deviation through themultiplication of the tuning parameter and the variance of the redcomponent. Also, in a case of detecting partition cells having a bluecomponent where a difference with respect to a global mean value of bluecomponents is greater than the reference deviation, the image signalprocessor 20 may calculate the reference deviation through themultiplication of the tuning parameter and the variance of the bluecomponent.

With respect to the embodiment described in relation to FIGS. 8A, 8B and8C, a variance may be based on calculation of the reference deviation,but is not limited thereto. For example, the reference deviation may becalculated based on a variation of coefficient or a standard deviationof the color components of the selected partition cells.

Then, the image signal processor 20 may be used to calculate, as a skintone estimation value, a final mean value of color components of thecolor-detected selected partition cells (S136); that is, excepting thethose partition cells failing color-detection. The final mean value maybe calculated based on the color components of the color-detectedselected partition cells, thereby providing a more accurate skin toneestimation value.

Referring to FIG. 8B, a distance ‘d’ between a global mean value G_Meanand a color component P of an arbitrary partition cell is shown in adistribution chart. The distribution chart may be, for example, adistribution chart of color components of the selected partition cellsin a normalized red (blue) space Normalized R(B).

When the distance ‘d’ between the global mean value G_Mean and the colorcomponent P of the partition cell is relatively short, the partitioncell may be determined to be a partition cell having a relativelydominant skin tone feature. In contrast, when the distance ‘d’ betweenthe global mean value G_Mean and the color component P of the partitioncell is relatively long, the partition cell may be determined asrelatively deviating from a feature of the skin tone.

Therefore, in order to calculate an accurate skin tone estimation value,partition cells where the distance ‘d’ from the global mean value G_Meanis greater than the reference deviation may be excluded. Therefore, afinal mean value of color components of partition cells having therelatively dominant feature of the skin tone among the selectedpartition cells may be calculated as a skin tone estimation value havingimproved accuracy.

Referring to FIG. 8C, a global mean value MeanOfRn of red components anda global mean value MeanOfBn of blue components of the selectedpartition cells, a variance VarRn of a red component and a varianceVarBn of a blue component of an arbitrary partition cell, a tuningparameter a, and a variance of each of a red component CurPatch.R and ablue component CurPatch.B of the arbitrary partition cell may bedeclared in the pseudo code (80 to 83). Also, a difference between thered component CurPatch.R and a global mean value MeanOfRn of the redcomponent of the arbitrary partition cell may be defined as a temporaryvariable TempDiffRn, and a difference between the blue componentCurPatch.B and a global mean value MeanOfBn of the blue component of thearbitrary partition cell may be defined as a temporary variableTempDiffBn.

Subsequently, in the color components of the arbitrary partition cell,whether a difference with respect to the global mean value MeanOfRn isgreater than the reference deviation VarRn*a and a difference withrespect to the global mean value MeanOfBn is greater than the referencedeviation VarBn*a may be determined based on a conditional statement (86to 89). For example, a reference deviation for comparison of the redcomponent may be a multiplication of the tuning parameter a and thevariance VarRn of the red component, and a reference deviation forcomparison of the blue component may be a multiplication of the tuningparameter a and the variance VarBn of the blue component.

In an exemplary embodiment, when the difference between the redcomponent CurPatch.R and the global mean value MeanOfRn of the arbitrarypartition cell is equal to or less than the reference deviation VarRn*aand the difference between the blue component CurPatch.B and the globalmean value MeanOfBn of the blue component of the arbitrary partitioncell is equal to or less than the reference deviation VarBn*a, thearbitrary partition cell may be based on the calculation of theestimation value of the skin tone (FdAvg(CurPatch)). Alternatively, whenthe difference with respect to the global mean value MeanOfRn is greaterthan the reference deviation VarRn*a and the difference with respect tothe global mean value MeanOfBn is greater than the reference deviationVarBn*a in the color components of the arbitrary partition cell, thearbitrary partition cell may be excluded from the calculation of theestimation value of the skin tone (EXC(CurPatch)).

FIG. 9 is a flowchart summarizing in one example the method step S30 ofFIG. 2 according to an exemplary embodiment. The method step andunderlying operation(s) described by FIG. 9 may be performed, forexample, by the image signal processor 20 of FIG. 1.

Referring to FIG. 9, the image signal processor 20 may select graycandidate partition cells included in a first gray candidate clusterdefined in a color space (S200). In an exemplary embodiment, the colorspace where the first gray candidate cluster is defined may beconfigured with a normalization value of each of a red (R) component anda blue (B) component with respect to a sum of the red (R) component, agreen (G) component, and the blue (B) component. Alternatively, thecolor space may be configured with a normalization value of each of thered (R) component and the blue (B) component with respect to the green(G) component.

Subsequently, the image signal processor 20 may select a second graycandidate cluster, based on the first gray candidate cluster and a skintone estimation value (S210). In an exemplary embodiment, if image dataD_IMG includes a face image, the skin tone estimation value may be amean value of color components of at least one of a plurality ofpartition cells included in the face image. The skin tone estimationvalue may include a red component estimation value and a blue componentestimation value associated with the skin tone.

In this context, an outlier for detection of a white point may beselected in the first gray candidate cluster, and the second graycandidate cluster may be the other region of the first gray candidatecluster, except the selected outlier. In other words, the selection ofthe second gray candidate cluster may be a process of narrowing (orfurther selecting) the first gray candidate cluster region in the colorspace.

In an exemplary embodiment, the image signal processor 20 may determinewhether to select a first partition cell of gray candidate partitioncells included in the first gray candidate cluster as a partition cellof the second gray candidate cluster, based on a red componentestimation value, a blue component estimation value, and a value of eachof a red component and a blue component of the first partition cell.This approach will be described in some additional detail hereafter.

Then, the image signal processor 20 may be used to select a white pointin the second gray candidate cluster (S220). The white point may be, forexample, a mean value of color components of partition cells included inthe second gray candidate cluster.

According to an exemplary embodiment, an outlier for detection of awhite point may be selected based on a skin tone estimation value andmay be excluded from the detection of the white point, therebypreventing the AWB from being performed based on an inaccurate skincolor. Also, partition cells relatively close to gray in image data maybe selected based on the skin tone, and thus, the AWB with improvedaccuracy is performed.

FIG. 10 is a graph showing a first gray candidate cluster defined in acolor space according to an exemplary embodiment. The X axis of thegraph represents a normalized red component (Normalized R), and the Yaxis of the graph represents a normalized blue component (Normalized B).

Referring to FIG. 10, a first gray candidate cluster G_1 may be definedin a color space including the normalized red component Normalized R andthe normalized blue component Normalized B. For example, in the graphshown in FIG. 10, the color space where a constant luminance Y isassumed, and a color information distribution of partition cells basedon a relative change in color component in a color space region isshown.

The normalized red component Normalized R may be, for example, R/(R+B+G)or R/G. Also, the normalized blue component Normalized B may be, forexample, B/(R+B+G) or B/G.

An image processing device such as the image signal processor 1 of FIG.1 may use partial data—that is, a mean value for sampled data instead ofusing whole data of a sensed image IMG. A white point may be calculatedbased on a mean value of a first gray candidate cluster G_1, and thus,in order to extract the white point by using the patching data, aselection of the first gray candidate cluster G_1 may be firstperformed.

The first gray candidate cluster G_1 may include a plurality of graycandidate partition cells PC_G. The gray candidate partition cells PC_Gmay be partition cells, of which a red component and a blue componentare all included in the first gray candidate cluster G_1, of a pluralityof partition cells included in image data D_IMG, for example.

In an exemplary embodiment, an estimation value FdAvg of a skin tone ofthe image data D_IMG may be included in the first gray candidate clusterG_1. The estimation value FdAvg may include a red component and a bluecomponent corresponding to the skin tone. In an exemplary embodiment,the estimation value FdAvg may be a mean value of color components ofsome of a plurality of partition cells included in a face image.

In an exemplary embodiment, which of a plurality of gray candidatepartition cells PC included in the first gray candidate cluster G_1 areselected as a gray candidate partition cell of a second gray candidatecluster may be determined based on the estimation value FdAvg and colorinformation included in each of the gray candidate partition cells PC.For example, gray candidate partition cells selected as the second graycandidate cluster may be based on calculation of a white point. Theselection of the second gray candidate cluster will be described below.

FIG. 11 is another graph further describing the selection of a secondgray candidate cluster according to an exemplary embodiment.

Referring to FIG. 11, a red component and a blue component of anarbitrary partition cell included in a first gray candidate cluster G_1may be respectively defined as CurrentPatch.R and CurrentPatch.B, and ared component and a blue component of an estimation value FdAvg may berespectively defined as FdAvg.R and FdAvg.B. In this case, partitioncells satisfying the following condition among a plurality of partitioncells included in the first gray candidate cluster G_1 may each beselected as an outlier OT.ABS(CurrentPatch.R−FdAvg.R)+ABS(CurrentPatch.B−FdAvg.B)<Constantvalue(TP)  (1)

In Equation (1), ABS denotes an absolute value, and TP denotes a tuningparameter. In other words, an arbitrary partition cell included in thefirst gray candidate cluster G_1 may be selected as an outlier when asum of an absolute value of a difference between a red component of thearbitrary partition cell and a red component of an estimation value andan absolute value of a difference between a blue component of thearbitrary partition cell and a blue component of the estimation value isless than the tuning parameter. For example, a region of the outlierbased on Equation (1) may have a diamond shape where estimation valueFdAvg coordinates correspond to a center. In an exemplary embodiment,the outlier may be excluded from a second gray candidate cluster and maybe excluded from calculation of a white point.

FIG. 12 is another graph further describing the selection of a secondgray candidate cluster according to another exemplary embodiment.

Referring to FIGS. 11 and 12, partition cells satisfying the followingcondition among a plurality of partition cells included in a first graycandidate cluster G_1 may each be selected as an outlier OT′.(CurrentPatch.R−CurrentPatch.B)>(FdAvg.R−FdAvg.B)  (2)

According to Equation (2), an arbitrary partition cell included in thefirst gray candidate cluster G_1 may be selected as an outlier OT′ whena difference between a red component and a blue component of thearbitrary partition cell is greater than a difference between a redcomponent and a blue component of an estimation value. For example, aregion of the outlier OT′ based on Equation (2) may be a first graycandidate cluster G_1 region which is located in a right region withrespect to a first straight line L1 crossing estimation value FdAvgcoordinates.

FIG. 13 is another graph further describing the selection of a secondgray candidate cluster according to another exemplary embodiment.

Referring to FIGS. 11 and 13, partition cells satisfying the followingcondition among a plurality of partition cells included in a first graycandidate cluster G_1 may each be selected as an outlier OT′″.(CurrentPatch.R>FdAvg.R)&&(CurrentPatch.B<FdAvg.B)   (3)

According to Equation (3), an arbitrary partition cell included in thefirst gray candidate cluster G_1 may be selected as an outlier OT′″ in acase of satisfying a condition where a red component of the arbitrarypartition cell is greater than a red component of an estimation valueand a blue component of the arbitrary partition cell is greater than ablue component of the estimation value. For example, a region of theoutlier OT′″ based on Equation (3) may be a first gray candidate clusterG_1 region which is located in a quadrant configured by a secondstraight line L2 and a third straight line L3 with respect to the secondstraight line L2 and the third straight line L3 which pass by estimationvalue FdAvg coordinates.

FIG. 14 is another graph further describing the selection of a secondgray candidate cluster according to an exemplary embodiment.

Referring to FIG. 14, a first gray candidate cluster G_1 may be definedin a color space, and a partial region of the first gray candidatecluster G_1 may be defined as a second gray candidate cluster G_2. In anexemplary embodiment, the second gray candidate cluster G_2 may beselected based on at least one of the embodiments of FIGS. 11 to 13. Inother words, a plurality of partition cells included in the second graycandidate cluster G_2 may satisfy at least one of the conditionsdisclosed in Equations (1) to (3).

For example, the second gray candidate cluster G_2 may be defined by afourth straight line L4 and a fifth straight line L5 in the first graycandidate cluster G_1. In detail, partition cells of the first graycandidate cluster G_1 which are located in a region between the fourthstraight line L4 and the fifth straight line L5 may be selected aspartition cells of the second gray candidate cluster G_2. The fourthstraight line L4 and the fifth straight line L5 may each be a virtualline configuring a boundary of the second gray candidate cluster G_2.

In an exemplary embodiment, the fourth straight line L4 may be adjustedalong a first arrow D1, based on one of Equations (1) to (3). Partitioncells excluded from the selection of the second gray candidate clusterG_2 with respect to the fourth straight line L4 may be, for example,partition cells including a relatively excessive red component.

In an exemplary embodiment, the fifth straight line L5 may be adjustedalong a second arrow D2, based on clustering-based statisticalprocessing of pieces of sample image data. For example, the fifthstraight line L5 may be adjusted along the second arrow D2, based onvarious statistical processing performed for excluding a partition cellincluding an excessive blue component by using the pieces of sampleimage data. Partition cells excluded from the selection of the secondgray candidate cluster G_2 with respect to the fifth straight line L5may be, for example, partition cells including a relatively excessiveblue component.

Therefore, partition cells included in the second gray candidate clusterG_2 may be based on a selection of a white point, and the otherpartition cells which are included in the first gray candidate clusterG_1 but are not selected as partition cells of the second gray candidatecluster G_2 may be excluded from the selection of the white point. Bydecreasing a region of the first gray candidate cluster G_1 to a regionof the second gray candidate cluster G_2, partition cells including arelatively excessive red or blue component may be excluded from theselection of the white point. Accordingly, a white point with improvedaccuracy may be selected.

FIG. 15 is a diagram illustrating an example wherein partition cellsincluded in a second gray candidate cluster selected according to anexemplary embodiment are displayed on an actual image.

Referring to FIG. 15, an image IMG may include a face image and may bedivided into a plurality of partition cells PC. Each of the partitioncells PC may be, for example, a patch or a pixel.

Some of the plurality of partition cells PC included in the image IMGmay each be selected as outlier for detection of a white point and maybe excluded from the detection of the white point. Hatched partitioncells of the plurality of partition cells PC included in the image IMGmay be gray candidate partition cells which are based on the detectionof the white point, and the other partition cells may be the partitioncells selected as the outlier. For example, the hatched partition cellsmay be partition cells included in the second gray candidate cluster G_2of FIG. 14.

For example, an outlier may be selected based on an estimation value ofa skin tone of a face image and may be excluded from the detection ofthe white point. As a result, partition cells, having a color componentsimilar to a skin color, of a plurality of partition cells included inan image may be excluded from the detection of the white point.Therefore, even when an inaccurate face color is included in the image,the AWB with improved accuracy may be performed.

FIG. 16 is a flowchart summarizing a method that may be executed by theimage signal processor 20 of FIG. 1 according to an exemplary embodimentfollowing the method previously described in relation to FIG. 9.

Referring to FIG. 16, the image signal processor 20 may add a weightvalue to partition cells, excluded from a selection of a second graycandidate cluster, of a plurality of gray candidate partition cells,based on a first reference value (S300). The gray candidate partitioncells may be, for example, partition cells included in a first graycandidate cluster.

For example, the image signal processor 20 may select a weight valuewithin a range of a first weight value or more and the second weightvalue or less and may add the selected weight value to the partitioncells, excluded from the selection of the second gray candidate cluster,of the plurality of gray candidate partition cells, based on the firstreference value. Each of the first weight value and the second weightvalue may be a constant, and the second weight value may be greater thanthe first weight value. In an exemplary embodiment, the first referencevalue may be calculated based on clustering-based statistical processingof pieces of sample image data.

For example, in the clustering-based statistical processing, the AWB maybe performed on the pieces of sample image data a plurality of times,and the first reference value which allows a partition cell having agray component to be balanced close to an ideal gray component may becalculated. The ideal gray component may include, for example, a redcomponent, a blue component, and a green component at the same ratio.

Thereafter, the image signal processor 20 may determine whether toreselect the partition cells excluded from the selection of the secondgray candidate cluster as partition cells of the second gray candidatecluster, based on the weight value (S310). For example, partition cellsto which an arbitrary weight value is added may be reselected as thepartition cells of the second gray candidate cluster, and partitioncells to which another arbitrary weight value is added may be excludedfrom the second gray candidate cluster. In an exemplary embodiment,partition cells to which the first weight value is added may not bereselected as the partition cells of the second gray candidate cluster,and at least some of partition cells to which a weight value greaterthan the first weight value is added may be reselected as the partitioncells of the second gray candidate cluster.

FIG. 17 is a flowchart summarizing a method based on a weight valueadded according to an exemplary embodiment. FIG. 17 may be understood asfurther describing on one example the method step S310 of FIG. 16.

Referring to FIG. 17, the image signal processor 20 may determinewhether a weight value added to an arbitrary gray candidate partitioncell is the first weight value (S311). When the first weight value isadded, a corresponding partition cell may be excluded from the selectionof the second gray candidate cluster (S312).

When the weight value added to the arbitrary gray candidate partitioncell is not the first weight value, the image signal processor 20 maydetermine whether the weight value added to a corresponding partitioncell is the second weight value (S313). For example, the second weightvalue may be greater than the first weight value. When the second weightvalue is added, the corresponding partition cell may be reselected as apartition cell of the second gray candidate cluster (S314).

When the weight value added to the arbitrary gray candidate partitioncell is not the first weight value or the second weight value, the imagesignal processor 20 may reflect the corresponding partition cell in theselection of the white point, based on the weight value added to thecorresponding partition cell (S315). In an exemplary embodiment, graycandidate partition cells to which a weight value instead of the firstand second weight values is added may be reflected at a lower level ofcontribution to the selection of the white point than the second graycandidate cluster.

For example, if instead of the first and second weight values, theweight value added to the gray candidate partition cells is 0.5,corresponding partition cells may contribute to the selection of thewhite point by half of a level of contribution of the second graycandidate cluster to the selection of the white point. For example,color components of the gray candidate partition cells to which theweight value instead of the first and second weight values is added maybe multiplied by the weight value added to the gray candidate partitioncells, and then, may be reflected in the selection of the white point.

Based on a reselection operation based on the added weight value,partition cells, used for the selection of the white point, of aplurality of partition cells selected as an outlier may be selected asthe partition cells of the second gray candidate cluster again.Accordingly, a white point with more improved accuracy is detected.

From the foregoing description of exemplary embodiments it may bereadily understood that the inventive concept provides for thecalculation and use of a skin tone estimation value that is much moreaccurate than previously provided. In various embodiments AWB may beperformed by an image signal processor, wherein the AWB includes thesteps of dividing image data including a face image into a plurality ofpartition cells, detecting a face region associated with the face imagefrom the image data, and selecting partition cells associated with theface image from among the plurality of partition cells to define a setof selected partition cells. However, rather than merely relying uponthis set of selected partition cells—which as associated with the faceimage—certain embodiments of the invention further define (or refine)the set of selected partition cells to define a set of further selectedpartition cells, and it is this second or further selected set ofpartition cells that is used to calculate a skin tone estimation value.

As has been described above there are many possible approaches to thisfurther selection of face image associated partition cells. For example,the further defining of the set of selected partition cells may include:(1) extracting vertex information for each partition cell in the set ofselected partition cells, and including in the set of further selectedpartition cells only those selected partition cells having all vertexesincluded in the face image; (2) excluding from the set of furtherselected partition cells those selected partition cells including atleast one of an eye and an eyebrow; (3) extracting respective luminancevalues for each of the selected partition cells, and excluding from theset of further selected partition cells those selected partition cellshaving a luminance value less a luminance threshold; and (4) calculatinga global mean value of color components for the set of selectedpartition cells, color-detecting each of the selected partition cells todetermine whether a color component for each of the selected partitioncells has a difference with the global mean value greater than areference deviation, and excluding from the set of further selectedpartition cells those selected partition cells having a color componentdifference with the global mean value greater than the referencedeviation.

FIG. 18 is a diagram illustrating an example of a mobile device 2000incorporating an image signal processor according to an exemplaryembodiment. The mobile device 2000 may be equipped with the image signalprocessor described above with reference to FIGS. 1 to 17. Therefore,the AWB may be performed on images captured by a camera 2300, accordingto the above-described embodiment.

The mobile device 2000 may be a smartphone in which functions are notlimited and a number of functions are changeable or extendable with anapplication program. The mobile device 2000 may include an embeddedantenna 2100 for exchanging a radio frequency (RF) signal with awireless base station, and moreover, may include a display screen 2200,such as a liquid crystal display (LCD) or an organic light emittingdiode (OLED) screen, for displaying images captured by the camera 2300or images received by the antenna 2100. The mobile device 2000 mayinclude an operation panel 2400 including a control button and a touchpanel. Also, if the display screen 2200 is a touch screen, the operationpanel 2400 may further include a touch sensing panel of the displayscreen 2200. The mobile device 2000 may include a speaker 2800 (oranother type of output unit) for outputting a voice and a sound and amicrophone 2500 (or another type of sound input unit) through which avoice and a sound are input.

The mobile device 2000 may further include the camera 2300, such as aCCD camera, for capturing an image or a video. Also, the mobile device2000 may include a storage medium 2700 for storing an image or videodata, which is captured by the camera 2300, received through an e-mail,or obtained through another manner, and a slot 2600 which enables thestorage medium 2700 to be equipped in the mobile device 2000. Thestorage medium 2700 may be a secure digital (SD) card or flash memorysuch as electrically erasable and programmable read only memory (EEPROM)embedded in a plastic case.

While the inventive concept has been particularly shown and describedwith reference to embodiments thereof, it will be understood thatvarious changes in form and details may be made therein withoutdeparting from the scope of the following claims.

What is claimed is:
 1. An auto white balance method performed by animage signal processor, the auto white balance method comprising:dividing image data, including a face image, into a plurality ofpartition cells; detecting a face region for the face image in the imagedata; selecting partition cells from among partition cells associatedwith the face region; calculating a skin tone estimation value for theimage data from the selected partition cells; selecting outlierpartition cells among the partition cells based on the skin toneestimation value, wherein outlier partition cells have a color componentsimilar to a skin color; selecting a white point within a portion of thepartition cells that excludes the outlier partition cells; and makingcolor sense adjustment to the image data in relation to the white point.2. The auto white balance method of claim 1, wherein the selecting ofthe partition cells further comprises selecting partition cells fromamong a plurality of partition cells having all vertexes included in theface region.
 3. The auto white balance method of claim 1, wherein theselecting of the partition cells further comprises excepting from theselected partition cells those partition cells among the partition cellsassociated with the face region which include at least one of an eyeimage and an eyebrow image.
 4. The auto white balance method of claim 1,wherein the selecting of the partition cells further comprises exceptingfrom the selected partition cells those partition cells among thepartition cells associated with the face region which have acorresponding luminance value equal to or less than a thresholdluminance.
 5. The auto white balance method of claim 1, wherein thecalculating of the skin tone estimation value comprises: calculating aglobal mean value of color components of the selected partition cells;and comparing the global mean value of color components of the selectedpartition cells to color-detect partition cells; identifying outliercolor-detected partition cells having a color component having adifference with respect to the global mean value of color components ofthe selected partition cells greater than a reference deviation; andcalculating a final mean value from the selected partition cells,excepting the outlier color-detected partition cells, as the skin toneestimation value.
 6. The auto white balance method of claim 5, furthercomprising: calculating a variance of the color component for each ofthe selected partition cells, wherein the reference deviation is amultiplication of the variance and a tuning parameter.
 7. The auto whitebalance method of claim 1, wherein each of the plurality of partitioncells is a patch including a plurality of pixels.
 8. The auto whitebalance method of claim 1, further comprising: adding a weight value toeach of the plurality of partition cells; and performing a reselectionon at least one of the selected outlier partition cells based on theweight value.
 9. An auto white balance method comprising: dividing imagedata including a face image into a plurality of partition cells;detecting a face region for the face image in the image data; selectingpartition cells from among partition cells associated with the faceregion; calculating a skin tone estimation value based on the selectedpartition cells included in the face region; selecting gray candidatepartition cells included in a first gray candidate cluster among theplurality of partition cells included in the image data, wherein thefirst gray candidate cluster is defined in a color space; selectingoutlier partition cells among the partition cells based on the skin toneestimation value, wherein the outlier partition cells have a colorcomponent similar to a skin color; selecting a second gray candidatecluster within a portion of the partition cells that excludes theoutlier partition cells; and performing auto white balance on the imagedata based on the second gray candidate cluster.
 10. The auto whitebalance method of claim 9, wherein the gray candidate partition cellsare selected as partition cells of the first gray candidate cluster,based on the skin tone estimation value.
 11. The auto white balancemethod of claim 9, wherein: the skin tone estimation value comprises ared component estimation value and a blue component estimation value ofa skin tone, and the selecting of the outlier partition cells comprisesdetermining whether to select, as an outlier partition cell, a firstpartition cell having a value of a red component and a value of a bluecomponent among the gray candidate partition cells, based on at leastone of the value of the red component, the value of the blue component,an estimation value of the red component, and an estimation value of theblue component.
 12. The auto white balance method of claim 11, whereinthe selecting of the outlier partition cells comprises, when a sum of:(1) an absolute value of a difference between the value of the redcomponent and the estimation value of the red component and (2) anabsolute value of a difference between the value of the blue componentand the estimation value of the blue component is less than a tuningparameter, selecting the first partition cell as the outlier partitioncell.
 13. The auto white balance method of claim 11, wherein theselecting of the outlier partition cells comprises, when a differencebetween the value of the red component and the value of the bluecomponent is greater than a difference between the estimation value ofthe red component and the estimation value of the blue component,selecting the first partition cell as the outlier partition cell. 14.The auto white balance method of claim 11, wherein the selecting of theoutlier partition cells comprises, when the value of the red componentis greater than the estimation value of the red component and the valueof the blue component is less than the estimation value of the bluecomponent, selecting the first partition cell as the outlier partitioncell.
 15. The auto white balance method of claim 9, wherein the colorspace is configured with a normalization value of each of a red (R)component and a blue (B) component with respect to a sum of the red (R)component, a green (G) component, and the blue (B) component.
 16. Theauto white balance method of claim 11, further comprising: adding aweight value to the outlier partition cells based on a first referencevalue calculated based on clustering-based statistical processing ofpieces of sample image data; and determining whether to reselect theoutlier partition cells as partition cells of the second gray candidatecluster, based on the weight value.
 17. The auto white balance method ofclaim 16, wherein: the adding of the weight value comprises adding, asthe weight value, one value within a range of a first weight value ormore and a second weight value or less, and the determining of whetherto reselect the partition cells comprises: excluding partition cells,where the weight value is the first weight value, from the selection ofthe gray candidate partition cells; and reselecting, as partition cellsof the first gray candidate cluster, at least some of partition cells,where the weight value is greater than the first weight value, of thepartition cells excluded from the selection of the gray candidatepartition cells.
 18. The auto white balance method of claim 9, whereinthe skin tone estimation value is a mean value of color components of atleast some of the plurality of partition cells included in the faceimage.
 19. An image processor comprising: an image sensor configured tocapture an image and output image data; and an image signal processorconfigured to: divide the image data, including a face image, into aplurality of partition cells; detect a face region for the face image inthe image data; select partition cells from among partition cellsassociated with the face region; calculate a skin tone estimation valuefor the image data from the selected partition cells; select outlierpartition cells among the partition cells based on the skin toneestimation value, wherein outlier partition cells have a color componentsimilar to a skin color; select a white point within a portion of thepartition cells that excludes the outlier partition cells; and makecolor sense adjustment to the image data in relation to the white point.20. An image processor of claim 19, wherein the image sensor comprisesat least one of a charge-coupled device (CCD) and/or a complementarymetal-oxide semiconductor (COMS) image sensor (CIS).